Large model training, especially for foundation models with billions or trillions of parameters, demands extremely high storage bandwidth to ensure efficient data ingestion, model checkpointing, and gradient synchronization across distributed systems. The storage bandwidth requirements are primarily influenced by the model size, batch size, training data volume, and parallelization strategy (e.g., data parallelism, model parallelism, or pipeline parallelism).
Key Factors Affecting Storage Bandwidth:
- Data Loading: Training datasets (often in TBs or PBs) must be streamed efficiently to GPUs/TPUs. High throughput is needed to prevent I/O bottlenecks.
- Checkpointing: Frequent saving of model weights (e.g., every few hours) requires fast write speeds to store large state files.
- Gradient Aggregation: In distributed training, gradients from multiple devices must be synchronized, demanding high read/write bandwidth for temporary storage.
- Mixed Precision & Caching: Storing optimized data formats (e.g., FP16, BF16) or caching frequently accessed data can reduce bandwidth pressure but still requires high baseline throughput.
Estimated Bandwidth Requirements:
- Small-Scale Models (1B-10B parameters): 10–100 GB/s (e.g., NVMe SSDs or high-performance NAS).
- Large-Scale Models (10B-1T+ parameters): 100 GB/s–1 TB/s or higher, often requiring parallel file systems (e.g., Lustre, GPFS) or distributed object storage with high IOPS.
Example:
Training a 175B-parameter model (like GPT-3) with a 32k batch size might require:
- Data Loading: 50–100 GB/s to feed tokenized datasets continuously.
- Checkpointing: 200–500 GB/s to save model snapshots (e.g., 700GB+ per checkpoint).
- Distributed Sync: Additional bandwidth for gradient exchange across hundreds of GPUs.
Recommended Solutions:
For such demanding workloads, Tencent Cloud offers high-performance storage services like:
- CBS (Cloud Block Storage): Low-latency SSDs for high IOPS, suitable for single-node or small-scale training.
- CFS (Cloud File Storage): Scalable NFS-based file systems with high throughput for shared access.
- COS (Cloud Object Storage): Cost-effective for storing massive datasets, paired with CDN acceleration for faster data retrieval.
- Tencent Cloud’s Distributed Storage Solutions: Optimized for AI/ML workloads, providing petabyte-scale bandwidth and low-latency access.
Additionally, leveraging RDMA (Remote Direct Memory Access) networks and NVMe-over-Fabrics can further enhance storage bandwidth in on-premises or hybrid cloud environments.